Similar case searching apparatus, relevance database generating apparatus, similar case searching method, and relevance database generating method

ABSTRACT

A similar case searching apparatus includes: a search vector generating unit which: with reference to a relevance database storing the degrees of relevance between (i) a combination of the keyword extracted by the keyword extracting unit and the attribute value of the keyword obtained by the keyword attribute obtaining unit and (ii) the respective image feature quantities extracted by the image feature extracting unit, performs weighting on (i) the image feature quantities extracted by the image feature extracting unit and (ii) image feature quantities extracted from a second medical image group of medical images included in a second case data item stored in the case database, using the degrees of relevance as weights; and a similar case searching unit which searches out, from the case database, the second case data item similar to a first case data item by comparing the weighed image feature quantities (i) and (ii).

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application of PCT International Application No.PCT/JP2012/001141 filed on Feb. 21, 2012, designating the United Statesof America, which is based on and claims priority of Japanese PatentApplication No. 2011-146698 filed on Jun. 30, 2011. The entiredisclosures of the above-identified applications, including thespecifications, drawings and claims are incorporated herein by referencein their entirety.

FIELD

One or more exemplary embodiments disclosed herein relate to a similarcase searching apparatus which automatically presents reference casesfor an interpretation target case and a relevance database generatingapparatus which generates a relevance database used by the similar casesearching apparatus.

BACKGROUND

Recently, in a medical diagnosis field, it is becoming easier fordoctors to share a large amount of data with an advancement ofdigitalization of medical images and image interpretation reports. Here,an image interpretation report is a text data item indicating adiagnosis made by an image interpreter such as a doctor based on amedical image. In addition, image interpretation reports stored inPicture Archiving and Communication Systems (PACS) which are systems forstoring and communicating images are managed in an associated manner,and the stored past image interpretation reports are desired to be usedsecondarily in an effective manner. A method for using such imageinterpretation reports secondarily is to automatically present referencecases for medical images which are interpretation targets based on whicha diagnosis is made. In relation to this, an effort for supporting adecision making related to a diagnosis is expected.

As a conventional technique for realizing presentation of such referencecases, Patent Literature 1 proposes a method of searching out andpresenting similar cases using image feature quantities of medicalimages corresponding to image interpretation reports stored in adatabase and text information included in the image interpolationreports. More specifically, in the search of the similar cases, a firstsearch is performed to search out image interpretation reports whichshow similar image forms from among the image interpretation reports.Subsequently, a second search is performed to extract representativekeywords between text information items in the image interpretationreports searched out in the first search, select image featurequantities associated in advance with the extracted keywords, andcalculate degrees of similarity between the cases based on the selectedimage feature quantities. The text information items described in theimage interpretation report show a viewpoint of the image interpreter.In other words, the method disclosed in Patent Literature 1 makes itpossible to present the similar cases searched out based on theviewpoint of the image interpreter, on condition that images andkeywords are associated with each other in advance.

CITATION LIST Patent Literature

-   [PTL 1]-   Japanese Unexamined Patent Application Publication No. 2009-093563

SUMMARY Technical Problem

However, the method disclosed in Patent Literature 1 does not make itpossible to associate image feature quantities and a keyword included inan image interpretation report in which a single image interpretationreport is assigned to a plurality of medical images. For this reason,the method has a problem that it is impossible to present similar casessearched out based on a viewpoint of an image interpreter and written inthe image interpretation report.

A contrast enhanced Computed Tomography (CT) scan is an example of adiagnosis in which a single image interpretation report is assigned to aplurality of medical images. In the contrast enhanced CT scan, theplurality of images are captured in time periods before and after anadministration of a contrast medium to a patient. An image interpreteror a doctor watches the captured images to check a temporal transitionof contrast enhancement effects, to generate the image interpretationreport.

The image interpretation report generated in this way includes a keywordassigned to all the images and a keyword assigned to particular one ormore of the images. More specifically, a keyword related to a diseasename such as “hepatocellular carcinoma” is a keyword assigned to all thecaptured images. On the other hand, a keyword related to image findings,for example “stain” or “low absorption” is a keyword assigned tospecific one or more of the images. These keywords are written in theimage interpretation report in a mixed manner, and thus it is impossibleto simply associate the keyword and the medical images.

As described above, the text information item described in the imageinterpretation report shows the viewpoint of the image interpreter. Inother words, when the keyword and the medical images are associatedmistakenly, it is impossible to present any similar case searched outbased on the viewpoint of the image interpreter.

In view of this, one non-limiting and exemplary embodiment provides asimilar case searching apparatus and a similar case searching methodwhich make it possible to search out similar cases based on a viewpointof an image interpreter (hereinafter also referred to as a user) such asa doctor in a userfriendly manner, even when a single imageinterpretation report is assigned to a plurality of medical images.

Furthermore, non-limiting and exemplary embodiments provide a relevancedatabase generating apparatus and a relevance database generating methodwhich make it possible to generate a relevance database which is used bythe similar case searching apparatus.

Solution to Problem

In one general aspect, the apparatus disclosed here features a similarcase searching apparatus which searches out, from a case database, atleast one second case data item similar to a first case data itemincluding a first medical image group of first medical images and afirst image interpretation report which is a text data item indicating aresult of interpreting the first medical image group, the similar casesearching apparatus including: an image feature extracting unitconfigured to extract a plurality of image feature quantities from thefirst medical image group; a keyword extracting unit configured toextract a keyword from the first image interpretation report, thekeyword being either (a) an image interpretation item which is acharacter string indicating a feature of at least one of the firstmedical images or (b) a disease name which is a result of a diagnosismade by a user based on the first medical images; a keyword attributeobtaining unit configured to obtain an attribute value which is a wordindicating a supplemental concept of the keyword, from a sentenceincluding the keyword extracted by the keyword extracting unit; a searchvector generating unit configured to: with reference to a relevancedatabase storing the degrees of relevance between (i) a combination ofthe keyword extracted by the keyword extracting unit and the attributevalue of the keyword obtained by the keyword attribute obtaining unitand (ii) the respective image feature quantities extracted by the imagefeature extracting unit, perform weighting on (i) the image featurequantities extracted by the image feature extracting unit and (ii) imagefeature quantities extracted from a second medical image group of secondmedical images included in the at least one second case data item storedin the case database, using the degrees of relevance as weights; andgenerate a search vector for the first medical image group and a searchvector for the second medical image group, each of the search vectorshaving, as elements, corresponding ones of image feature quantitiesresulting from the weighting; and a similar case searching unitconfigured to search out the at least one second case data item storedin the case database, based on a degree of similarity between the searchvector for the first medical image group and the search vector for thesecond medical group.

These general and specific aspects may be implemented using a system, amethod, an integrated circuit, a computer program, or acomputer-readable recording medium such as a computer-readable CD-ROM,or any combination of systems, methods, integrated circuits, computerprograms, or computer-readable recording media.

Additional benefits and advantages of the disclosed embodiments will beapparent from the Specification and Drawings. The benefits and/oradvantages may be individually obtained by the various embodiments andfeatures of the Specification and Drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

Advantageous Effects

The similar case searching apparatus according to one or more exemplaryembodiments or features disclosed herein is capable of searching outsimilar cases based on a viewpoint of an image interpreter in auserfriendly manner, even when a single image interpretation report isassigned to a plurality of medical images.

BRIEF DESCRIPTION OF DRAWINGS

These and other advantages and features will become apparent from thefollowing description thereof taken in conjunction with the accompanyingDrawings, by way of non-limiting examples of embodiments disclosedherein.

FIG. 1 is a block diagram showing a functional structure of a similarcase searching apparatus according to Embodiment 1.

FIG. 2 is a diagram showing an example of a case data item stored in acase database.

FIG. 3 is a diagram showing an example of a keyword dictionary.

FIG. 4 is a diagram showing an example of an attribute dictionary.

FIG. 5 is a diagram showing an example of a relevance database.

FIG. 6 is a flowchart of overall processes performed by the similar casesearching apparatus according to Embodiment 1.

FIG. 7 is a flowchart of detailed processes of a keyword attributeobtainment process (Step S103 in FIG. 6).

FIG. 8 is a diagram showing an example of a display screen output ontothe output medium by the output unit.

FIG. 9 is a diagram showing an example of a display screen output ontothe output medium by the output unit.

FIG. 10 is a block diagram showing a functional structure of a relevancedatabase generating apparatus according to Embodiment 2.

FIG. 11 is a flowchart of overall processes performed by the relevancedatabase generating apparatus according to Embodiment 2.

FIG. 12 is a diagram showing an example of a data table in which timephase attribute values and image capturing times are associated witheach other.

FIG. 13 is a conceptual diagram of correlation ratios between a keywordand image feature quantities.

FIG. 14 is a diagram showing a relationship between the similar casesearching apparatus according to Embodiment 1 and the relevance databasegenerating apparatus according to Embodiment 2.

FIG. 15 is a block diagram showing a functional structure of a relevancedatabase generating apparatus according to Embodiment 3.

FIG. 16 is a flowchart of overall processes performed by a relevancedatabase generating apparatus according to Embodiment 3.

FIG. 17 is a block diagram showing a hardware structure of a computersystem which includes either the similar case searching apparatusaccording to Embodiment 1 or the relevance database generating apparatusaccording to Embodiment 2 or 3.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments are described in detail with reference to thedrawings. It is to be noted that each of the embodiments described belowis a non-limiting general or specific example. The numerical values,structural elements, the arrangement and connection of the structuralelements, steps, the processing order of the steps etc. shown in thefollowing exemplary embodiments are mere examples, and therefore do notlimit the scope of the Claims. Therefore, among the structural elementsin the following exemplary embodiment, structural elements not recitedin any one of the independent claims which define the generic concept ofthe present disclosure are described as arbitrary structural elements.

The similar case searching apparatus according to an embodiment of thepresent disclosure is intended to search out similar cases for a casepresented by medical images interpreted by an image interpreter. Themedical images are, for example, ultrasonic images, CT images, ornuclear magnetic resonance images.

In one general aspect, the apparatus disclosed here features a similarcase searching apparatus which searches out, from a case database, atleast one second case data item similar to a first case data itemincluding a first medical image group of first medical images and afirst image interpretation report which is a text data item indicating aresult of interpreting the first medical image group, the similar casesearching apparatus including: an image feature extracting unitconfigured to extract a plurality of image feature quantities from thefirst medical image group; a keyword extracting unit configured toextract a keyword from the first image interpretation report, thekeyword being either (a) an image interpretation item which is acharacter string indicating a feature of at least one of the firstmedical images or (b) a disease name which is a result of a diagnosismade by a user based on the first medical images; a keyword attributeobtaining unit configured to obtain an attribute value which is a wordindicating a supplemental concept of the keyword, from a sentenceincluding the keyword extracted by the keyword extracting unit; a searchvector generating unit configured to: with reference to a relevancedatabase storing the degrees of relevance between (i) a combination ofthe keyword extracted by the keyword extracting unit and the attributevalue of the keyword obtained by the keyword attribute obtaining unitand (ii) the respective image feature quantities extracted by the imagefeature extracting unit, perform weighting on (i) the image featurequantities extracted by the image feature extracting unit and (ii) imagefeature quantities extracted from a second medical image group of secondmedical images included in the at least one second case data item storedin the case database, using the degrees of relevance as weights; andgenerate a search vector for the first medical image group and a searchvector for the second medical image group, each of the search vectorshaving, as elements, corresponding ones of image feature quantitiesresulting from the weighting; and a similar case searching unitconfigured to search out the at least one second case data item storedin the case database, based on a degree of similarity between the searchvector for the first medical image group and the search vector for thesecond medical group.

With this structure, weighting is performed on the image featurequantities in the generation of the search vectors. At this time, theweights used in the weighting are (i) combinations of a keywordextracted from the first image interpretation report and an attributevalue for the keyword, and (ii) the degrees of relevance with imagefeature quantities. The keyword and attribute value are valuesindicating a viewpoint of the image interpreter. For this reason, it ispossible to search out the similar cases based on the viewpoint of theimage interpreter. In addition, the attribute value indicates asupplemental concept of the keyword. For this reason, the attributevalue is a clue for finding out which one of the first medical images inthe first medical image group was used as a basis of the imageinterpretation report. Thus, it is possible to search out the similarcases based on the viewpoint of the image interpreter, even for the caseincluding the plurality of medical images and the single imageinterpretation report assigned to the medical images.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, a time phase attributevalue indicating a relative image capturing time of a corresponding oneof the first medical images in the first medical image group or arelative image capturing time period in which the corresponding firstmedical image is captured, from the sentence including the keywordextracted by the keyword extracting unit.

The use of the time phase attribute value as the attribute value makesit possible to find out which one of the first medical images in thefirst medical image group was used as a basis of the imageinterpretation report. For this reason, it is possible to search out thesimilar cases based on the viewpoint of the image interpreter, even forthe case including the plurality of medical images and the single imageinterpretation report assigned to the first medical image group.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, an existence attributevalue indicating existence or non-existence of information in the atleast one of the first medical images, from the sentence including thekeyword indicating the information and extracted by the keywordextracting unit.

When one of the first medical images includes information indicated byan image interpretation item but another one of the first medical imagesdoes not include information indicated by an image interpretation item,the use of the existence attribute value as the attribute value makes itpossible to find out which one of the first medical images in the firstmedical image group was used as a basis of the image interpretationreport. For this reason, it is possible to search out the similar casesbased on the viewpoint of the image interpreter, even for the caseincluding the plurality of medical images and the single imageinterpretation report assigned to the first medical image group.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, a portion attribute valueindicating a portion of an organ whose image is to be interpreted, fromthe sentence including the keyword extracted by the keyword extractingunit.

When first medical images to be interpreted show different portions ofan organ, the use of the portion attribute value as the attribute valuemakes it possible to find out which one of the first medical images inthe first medical image group was used as a basis of the imageinterpretation report. For this reason, it is possible to search out thesimilar cases based on the viewpoint of the image interpreter, even forthe case including the plurality of medical images and the single imageinterpretation report assigned to the first medical image group.

For example, the similar case searching apparatus may further include anoutput unit configured to output the at least one second case data itemsearched out by the similar case searching unit to outside the similarcase searching apparatus.

For example, the output unit may be configured to classify each of theat least one second case data item searched out by the similar casesearching unit into a corresponding one of case data item groups eachbased on similar diseases names, and output the classified at least onesecond case data item to outside the similar case searching apparatus.

The image interpreter needs to find out, using the results of searchingthe similar cases, a disease name description different from thediagnosis by himself or herself from the findings in the search resultswhen considering a possibility of a disease other than the disease asthe result of the diagnosis (image interpretation) by himself orherself. By presenting the search results for each of case data itemgroups each classified based on similar diseases names, the imageinterpreter can easily check the disease names in the cases presented asthe search results, and can reduce image interpretation time.

For example, the similar case searching unit may be configured to searchfor only one or more second case data items each assigned with an imageinterpretation report in which an image finding and a definitivediagnosis match from among second case data items stored in the casedatabase, the image finding may be a result of a diagnosis made by animage interpreter based on the second medical image group included inthe second case data item, and the definitive diagnosis may be a finaldiagnosis result obtained based on the second medical image groupincluded in the second case data item.

The image findings are results of a diagnosis by the image interpreteron the first medical images included in the case data item, and thedefinitive diagnosis is the final diagnosis result on the first medialimages included in the case data item. The case database includes themedical images based only on which it is impossible to indicate a lesionthat matches the definitive diagnosis, due to image noise orcharacteristics of an imaging device. There is a high possibility thatit is difficult to estimate a lesion based only on such medical images.Thus, presentation of such a similar case data item may increase therisk of a misdiagnosis. In contrast, each of case data items in whichimage findings and a definitive diagnosis match is a case data itemwhich guarantees that it is possible to point out the same lesion as thelesion in the definitive diagnosis from the medical images. Thus, thecase data item is appropriate as a similar case data item. Thus, bydetermining, as the search targets, only the case data items in each ofwhich image findings and a definitive diagnosis match, it is possible toreduce the risk of a misdiagnosis made with reference to similar cases.

In one general aspect, the apparatus disclosed here features a relevancedatabase generating apparatus, including: an image feature extractingunit configured to extract image feature quantities from a plurality ofmedical images; an image feature attribute obtaining unit configured toobtain attribute values of the respective image feature quantitiesextracted by the image feature extracting unit, from the plurality ofmedical images; a keyword extracting unit configured to extract, as akeyword, either an image interpretation item or a disease name, from animage interpretation report which is a text data item describing aresult of interpretation of the medical images by a user, the imageinterpretation item being a character string indicating a feature of atleast one of the medical images, and the disease name being a result ofa diagnosis made by the user based on the medical images; a keywordattribute obtaining unit configured to obtain an attribute value of thekeyword, from a sentence including the keyword extracted by the keywordextracting unit; a same attribute data generating unit configured togenerate a combination of the keyword and each of image featurequantities both having a same attribute value, based on (i) the keywordand the attribute value of the keyword extracted from the imageinterpretation report and (ii) the image feature quantities extractedfrom the medical images and the attribute values of the respective imagefeature quantities; and a relevance calculating unit configured tocalculate, from the combination of the keyword and each of the imagefeature quantities both having the same attribute value, a degree ofrelevance between the keyword and the image feature quantity, andgenerate a relevance database indicating a degree of relevance between(i) the combination of the keyword and the attribute value of thekeyword and (ii) the image feature quantity.

With this structure, the degrees of relevance are calculated between thekeyword and image feature quantities having the same attribute value.For this reason, it is possible to generate the relevance database which(i) stores degrees of relevance between combinations of keywords andattribute values thereof and image feature quantities and (ii) is foruse by the aforementioned similar case searching apparatus.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, a time phase attributevalue indicating a relative image capturing time of a corresponding oneof the medical images or a relative image capturing time period in whichthe corresponding medical image is captured, from the sentence includingthe keyword extracted by the keyword extracting unit.

The use of the time phase attribute value as the attribute value makesit possible to generate the relevance database with reference to whichit is possible to find out which one of the medical images in themedical image group was used as a basis of the image interpretationreport. For this reason, the similar case searching apparatus can searchout the similar cases based on the viewpoint of the image interpreter,even for the case including the plurality of medical images and thesingle image interpretation report assigned to the medical images.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, an existence attributevalue indicating existence or non-existence of information shown by thekeyword, from the sentence including the keyword, the keyword indicatingthe feature of the at least one of the medical images and extracted bythe keyword extracting unit.

When one of medical images includes information indicated by an imageinterpretation item but another one of the medical images does notinclude information indicated by an image interpretation item, the useof the existence attribute value as the attribute value makes itpossible to generate the relevance database with reference to which itis possible to find out which one of the medical images was used as abasis of the image interpretation report. For this reason, the similarcase searching apparatus can search out the similar cases based on theviewpoint of the image interpreter, even for the case including theplurality of medical images and the single image interpretation reportassigned to the medical images.

For example, the keyword attribute obtaining unit may be configured toobtain, as the attribute value of the keyword, a portion attribute valueindicating a portion of an organ whose image is to be interpreted, fromthe sentence including the keyword extracted by the keyword extractingunit.

When medical images to be interpreted show different portions of anorgan, the use of the portion attribute value as the attribute valuemakes it possible to generate the relevance database with reference towhich it is possible to find out which one of the medical images wasused as a basis of the image interpretation report. For this reason, thesimilar case searching apparatus can search out the similar cases basedon the viewpoint of the image interpreter, even for the case includingthe plurality of medical images and the single image interpretationreport assigned to the medical images.

As a specific example, the image feature attribute obtaining unit may beconfigured to obtain time phase attribute values as the attribute valuesof the respective image feature quantities extracted by the imagefeature extracting unit, from image capturing times of the respectivemedical images, with reference to a data table in which the imagecapturing times of the medical images and the time phase attributevalues are associated with each other.

For example, the relevance database generating apparatus may furtherinclude an interpretation target obtaining unit configured to obtain aplurality of medical images and an image interpolation reportcorresponding to the medical images included in a case data item, from acase database storing the case data item; and an update control unitconfigured to cause the interpretation target obtaining unit to obtain,at the time of update of the case database, the medical images and theimage interpretation report stored in the case database, wherein theimage feature extracting unit may be configured to extract image featurequantities from the medical images obtained by the interpretation targetobtaining unit, and the keyword extracting unit may be configured toextract the keyword from the image interpretation report obtained by theinterpretation target obtaining unit.

With this structure, when the case database is updated, the medicalimages and image interpretation report are obtained from the casedatabase. In response to this update, the relevance database storing thedegrees of relevance between combinations of keywords and attributevalues thereof and image feature quantities is also updated. Thus, thesimilar case searching apparatus can search out the similar cases basedon the viewpoint of the image interpreter.

For example, the update control unit may be configured to cause theinterpretation target obtaining unit to obtain, at the time of theupdate of the case database, medical images and image interpretationreports included in all case data items included in the case database.

For example, the update control unit may be configured to cause theinterpretation target obtaining unit to obtain, at the time of theupdate of the case database, (i) one or more image interpretationreports each including a keyword having an appearance frequency no morethan a threshold value among all image interpretation reports stored inthe case database and (ii) medical images corresponding to the one ormore image interpretation reports.

In the case of a keyword having a low appearance frequency, theuncertainty of the degree of relevance is high, and thus the necessityof updating the degree of relevance is high. In this way, by determiningwhether or not update is allowed according to the appearance frequencyof each keyword in the case database, it is possible to reduce thecalculation amount at the time of update, and to thereby reduce theupdate time.

Embodiment 1

First, terms used in Embodiments 1 to 3 are described.

The “image feature quantities” are extracted from medical image, andrelate to, for example, the shapes of organs or lesion portions in themedical images, or the luminance distributions of the medical images.For example, Non-patent Literature describes the use of four hundred andninety kinds of feature quantities (Non-patent Literature 2:“Improvement of Tumor Detection Performance in Mammograms by FeatureSelection from a Large Number of Features and Proposal of Fast FeatureSelection Method”, by Nemoto, Shimizu, Hagihara, Kobatake, and Nawano,The Journal of the Institute of Electronics, Information andCommunication Engineers (J. IEICE) D-II, Vol. J88-D-II, No, 2, pp.416-426, February 2005). As image feature quantities used in thisembodiment, several ten to several hundred kinds of image featurequantities are predefined for each of medical image capturingapparatuses (modality apparatuses) used to capture the medical images oreach of target organs used for image interpretation.

A “Keyword” shows any one of an “image interpretation item” and a“disease name” described below.

An “image interpretation item” is defined in this Description as a“character string made by an image interpreter (such as a doctor) asverbally indicating a feature of a interpretation-target medical image”.Terms that are used as image interpretation items are limited withincertain ranges for the respective medical image capturing apparatuses,target organs, or the like. Examples of the image interpretation itemsinclude: Lobular, Spinal, Irregular, Clear border, Unclear contour, Lowdensity, High density, Low absorption, High absorption, Ground-glassopacity, Calcification, Mosaic pattern, Early stain, Low echo, Highecho, and Fuzz.

A “disease name” is the name of a disease diagnosed by the imageinterpreter (such as the doctor) based on medical images and othermedical tests. Examples of disease names include hepatocellularcarcinoma, cyst, angioma, and so on.

An “attribute” is a word showing a supplemental concept of a keyword.More specifically, attributes are classified into three kinds of timephase attribute, existence attribute, and portion attribute. It is to benoted that an “attribute” is also obtained from an image featurequantity.

A time phase attribute is a concept associated with image capturing timeby a test device, or time from when a contrast medium is infused to whenimage capturing is performed (or to image capturing timing). Forexample, in the case of a dynamic CT scan using a contrast medium,either an arterial phase, an equilibrium phase, or the like correspondsto the attribute value of a time phase attribute. In other words, a timephase attribute value indicates a relative image capturing time of acorresponding one of the plurality of medical images or a relative imagecapturing time period in which the corresponding medical image iscaptured.

An existence attribute is a concept showing whether or not an imageinterpretation item or a disease name exists or not. The attributevalues of existence attributes correspond to “existence” and“non-existence”. For example, a sentence that “A stain is recognized”means that a keyword of “stain” “exists”, and “is recognized” is acharacter string information item indicating the attribute value of“existence”. In addition, a sentence that “A stain is not recognized”means that a keyword of “stain” does not “exist”, and “is notrecognized” is a character string information item indicating theattribute value of “non-existence”.

A portion attribute is a concept indicating an organ, the position ofthe organ, or a partial area of the organ. For example, a “liver” or a“liver S1 segment” corresponds to the attribute value of a portionattribute.

Embodiment 1 Explanation of Structure

Hereinafter, a similar case searching apparatus according to Embodiment1 is described in detail with reference to the drawings.

FIG. 1 is a block diagram showing a functional structure of the similarcase searching apparatus according to Embodiment 1.

The similar case searching apparatus 100 is configured to search outcase data items (hereinafter also referred to as “cases”) according toresults of image interpretation by an image interpreter.

The similar case searching apparatus 100 includes: an interpretationtarget obtaining unit 105; a keyword extracting unit 106; a keywordattribute obtaining unit 107; an image feature extracting unit 108; asearch vector generating unit 109; a similar case searching unit 110;and an output unit 111. The similar case searching apparatus 100 isconnected to a case database 101, a keyword dictionary 102, an attributedictionary 103, and a relevance database 104 which are locatedexternally.

Hereinafter, detailed descriptions are sequentially given of structuralelements of the case database 101 and the similar case searchingapparatus 100 shown in FIG. 1.

The case database 101 is stored in a storage device including a harddisk, a memory, or the like. The case database 101 is a database storinga plurality of case data items each including a plurality of medicalimages showing images of an interpretation target to be presented to animage interpreter and a single image interpretation report correspondingto the plurality of medical images. Here, the plurality of medicalimages are image data items used for an image-based diagnosis, and arestored in an electronic medium. In this Description, the image dataitems may be simply referred to as images. In addition, the imageinterpretation report is information indicating a result of interpretingthe medical images and a definitive diagnosis resulting from a biopsyperformed after the image-based diagnosis. The image interpretationreport is document data (text data). A biopsy is a medical test which isperformed using a microscope or the like to examine an extracted part ofa lesion.

FIG. 2 shows exemplary CT images as a medical image group 20 and anexemplary image interpretation report 21 which are included in case dataitems stored in the case database 101. The medical image group 20 iscomposed of a plurality of medical images. The image interpretationreport 21 includes an image interpretation report ID 22, an image ID 23,image findings 24, and a definitive diagnosis 25.

The image interpretation report ID 22 is an identifier for identifyingthe image interpretation report 21. The image ID 23 is an identifier foridentifying the medical image group 20. The image findings 24 isinformation indicating a result of diagnosis based on the medical imagegroup 20 having the image ID 23. In other words, the image findings 24is information indicating the result of the diagnosis (imageinterpretation result) and a basis for the diagnosis (a basis for theimage interpretation), including image interpretation items and adisease name. The definitive diagnosis 25 shows a definitive diagnosisfor a patient indicated by the image interpretation report ID 22. Here,the definitive diagnosis is the final diagnosis result clearly showingthe real state of the disease of the target patient by performing thepathological test using a microscope onto the test body obtained in asurgery or a biopsy or through other various kinds of means.

The keyword dictionary 102 is stored in, for example, a storage deviceincluding a hard disk, a memory, or the like. The keyword dictionary 102is a database storing keywords extracted from the image interpretationreports 21. FIG. 3 is a diagram showing an example of the keyworddictionary 102. As shown in FIG. 3, the keyword dictionary 102 storeskeywords 30 in a list form.

The attribute dictionary 103 is stored in, for example, a storage deviceincluding a hard disk, a memory, or the like. The attribute dictionary103 is a database storing attribute values and target words with theattribute values extracted from the image interpretation reports 21.FIG. 4 is a diagram showing an example of the attribute dictionary 103.As shown in FIG. 4, the attribute dictionary 103 stores predeterminedattributes 40 and target words 42 corresponding to the attribute values41 in an associated form. For example, the attribute values of timephase attributes indicating the image capturing times of the respectivemedical images included in the medical image group 20 are a simplephase, an arterial phase, and an equilibrium phase. When one of theimage interpretation reports 21 includes a word that is an arterialphase or an early phase, the attribute value of the time phase attributeis the arterial phase. When one of the image interpretation reports 21includes a word that is an equilibrium phase or a late phase, theattribute value of the time phase attribute is the equilibrium phase.

The relevance database 104 is stored in a storage device including ahard disk, a memory, or the like.

The relevance database 104 is a database storing keywords and attributesextracted from image interpretation reports 21 and image featurequantities and degrees of relevance extracted from medical image groups20. FIG. 5 is a diagram showing an example of the relevance database104. As shown in FIG. 5, the relevance database 104 stores combinationsof keywords 50 and attribute values 51 extracted from one of the currentimage interpretation reports 21 and degrees of relevance with imagefeature quantities 52. A larger degree of relevance shows a higherrelevance between the both.

The interpretation target obtaining unit 105 obtains, from the casedatabase 101, one of the medical image groups 20 which was used by animage interpreter when making a diagnosis and the image interpretationreport 21. For example, information input through a keyboard, a mouse,or the like is stored in a memory or the like. Next, the interpretationtarget obtaining unit 105 outputs the obtained medical image group 20and image interpretation report 21 to the keyword extracting unit 106and the image feature extracting unit 108.

The keyword extracting unit 106 extracts, with reference to the keyworddictionary 102, keywords from the image interpretation report 21obtained by the interpretation target obtaining unit 105, and outputsthe extracted keywords and image interpretation report 21 to the keywordattribute obtaining unit 107. A specific keyword extracting method isdescribed later.

The keyword attribute obtaining unit 107 obtains the attribute values ofthe respective keywords, using the keywords and image interpretationreport 21 obtained from the keyword extracting unit 106 and theattribute dictionary 103, and outputs the obtained combinations ofkeywords and attribute values to the search vector generating unit 109.A specific keyword attribute value obtaining method is described later.

The image feature extracting unit 108 calculates image featurequantities for the medical image group 20 obtained by the interpretationtarget obtaining unit 105, and outputs the calculated image featurequantities to the search vector generating unit 109. A specific imagefeature quantity calculating method is described later.

The search vector generating unit 109 generates a search vector usingthe combinations of keywords and attribute values obtained from thekeyword attribute obtaining unit 107, the image feature quantitiesobtained from the image feature extracting unit 108, and the relevancedatabase 104. The search vector generating unit 109 outputs thegenerated search vector to the similar case searching unit 110. Here, asearch vector is a vector having, as an element, an image featurequantity for which weighting is performed using a degree of relevance ofan image feature quantity shown in the relevance database 104. Aspecific search vector generating method is described later. It is to benoted that a search vector is generated for each of a first medicalimage group of first medical images in a case (a first case) which is aninterpretation target and each of second medical image groups of secondmedical images in cases (second cases) stored in the case database 101.

The similar case searching unit 110 searches out a second case having ahigh degree of relevance with the first case as the interpretationtarget, from among the cases stored in the case database 101, using thesearch vectors obtained from the search vector generating unit 109. Thesimilar case searching unit 110 outputs the image interpretation reportID 22 of the searched-out second case to the output unit 111. A specificimage similarity calculating method is described later.

The output unit 111 outputs the image interpretation report ID obtainedfrom the similar case searching unit 110 to an output-destination mediumlocated externally. The output-destination medium is, for example, amonitor such as a liquid crystal display and a CRT. The imageinterpreter can check the case data item when the case data item isoutput to the output-destination medium.

Next, descriptions are given of operations performed by the similar casesearching apparatus 100 configured as described above.

Embodiment 1 Explanation of Operations

FIG. 6 is a flowchart of overall processes performed by the similar casesearching apparatus 100.

First, the interpretation target obtaining unit 105 obtains a firstmedical image group of first medical images which are interpretationtargets and a first image interpretation report which is an imageinterpretation report for the first medical image group. In other words,the interpretation target obtaining unit 105 obtains, from the casedatabase 101, the medical image group 20 and the image interpretationreport 21 used by the image interpreter when the diagnosis was made, asthe first medical image group and the first image interpretation report.The interpretation target obtaining unit 105 outputs the obtainedmedical image group 20 (first medical image group) and the imageinterpretation report 21 (first image interpretation report) to theimage feature extracting unit 108 and the keyword extracting unit 106(Step S101).

It is good that the medical image group 20 and the image interpretationreport 21 are obtained after the completion of the diagnosis by theimage interpreter. In this way, the image interpreter can automaticallycheck similar cases after the completion of the diagnosis.

In addition, the interpretation target obtaining unit 105 may obtain themedical image group 20 and the image interpretation report 21 of anarbitrary case selected by the image interpreter even if the case is theone diagnosed by a person other than the image interpreter as long asthe case has been already stored in the case database 101. In this way,it is possible to search out the similar cases from among the casesdiagnosed by the persons other than the user, and to thereby use themfor example, in occasions such as in a conference other than the time ofimage interpretation.

Next, the keyword extracting unit 106 extracts keywords from the imageinterpretation report 21 obtained from the interpretation targetobtaining unit 105, with reference to the keyword dictionary 102, andoutputs the extracted keywords and the image interpretation report 21 tothe keyword attribute obtaining unit 107 (Step S102). For example, whena word “stain” is included in the image interpretation report 21, thekeyword extracting unit 106 extracts the “stain” as a keyword, withreference to the keyword dictionary 102 shown in FIG. 3.

Next, the keyword attribute obtaining unit 107 obtains the attributevalue of each of the keywords, using the keywords and imageinterpretation report 21 obtained from the keyword extracting unit 106and the attribute dictionary 103. The keyword attribute obtaining unit107 outputs the combinations of the keyword and the attribute value tothe search vector generating unit 109 (Step S103).

FIG. 7 is a flowchart of detailed processes of a keyword attributeobtainment process (Step S103 in FIG. 6).

First, the keyword attribute obtaining unit 107 selects one keyword fromamong the keywords obtained from the keyword extracting unit 106 (StepS201).

Next, the keyword attribute obtaining unit 107 extracts the sentenceincluding the keyword selected in Step S201 from the imageinterpretation report 21 obtained from the keyword extracting unit 106(Step S202). As an exemplary specific sentence extraction process, it isgood to extract punctuations etc. such as “line feed marks” and“periods” located before and after the selected keyword, and extracttext between the extracted “line feed marks” or “periods” as a sentence.

Next, the keyword attribute obtaining unit 107 obtains the time phaseattribute value of the keyword selected in Step S201 from the sentenceextracted in Step S202 (Step S203). More specifically, with reference tothe attribute dictionary 103, the keyword attribute obtaining unit 107extracts a target word with a time phase attribute from the sentenceextracted in Step S202 and obtains the value of the time phase attributecorresponding to the extracted target word. For example, when thesentence includes a word such as an arterial phase or an early phase,the keyword attribute obtaining unit 107 obtains the arterial phase asthe time phase attribute value when extracting the time phase attributevalue using the attribute dictionary 103 shown in FIG. 4.

Here, when the keyword extracted by the keyword extracting unit 106relates to a disease name, it is also good to assign all of time phasesas attributes. The disease name is obviously a keyword determined basedon information about all the medical images, and thus it is possible toprevent an incorrect attribute from being assigned.

Next, the keyword attribute obtaining unit 107 obtains an existenceattribute value of the keyword selected in Step S201 from the sentenceextracted in Step S202 (Step S204). More specifically, with reference tothe attribute dictionary 103, the keyword attribute obtaining unit 107extracts a target word with the existence attribute from the sentenceextracted in Step S202 and obtains the value of the existence attributecorresponding to the extracted target word.

When the target word with the existence attribute is not included in thesentence, it is only necessary to assign an attribute value “existence”to the keyword selected in Step S201. The image interpretation reportincludes many incomplete sentences without verbs. For example, adescription “Stain in liver S1 segment” is written only when the stain“exists”, and therefore “found” or “recognized” explicitly indicatingthe existence is omitted. When no stain exists, a word with thenon-existence is added thereto. An exemplary description “No stain inliver S1 segment” is written. Thus, when a current sentence does notinclude any word with the non-existence, it is only necessary to assignan attribute value “existence” to the keyword selected in Step S201.

Next, the keyword attribute obtaining unit 107 obtains a portionattribute value of the keyword selected in Step S201 from the sentenceextracted in Step S202 (Step S205). More specifically, with reference tothe attribute dictionary 103, the keyword attribute obtaining unit 107extracts a target word with a portion attribute from the sentenceextracted in Step S202, and obtains the value of the portion attributecorresponding to the extracted target word (Step S205).

When the sentence does not include any word with the portion attribute,for example, it is also good to sequentially search for sentences beforethe keyword selected in Step S201, and obtain the portion attributevalue searched out initially as the portion attribute value. Forexample, in the sentences of “A stain is recognized in an early phase ofliver segment S1. Washout in a late phase”, the keyword “washout” doesnot have any portion attribute value. However, since the previoussentence has a portion attribute value “liver segment S1”, it ispossible to assign the portion attribute value to “washout”.

In addition, when a current sentence does not include any word with aportion attribute, for example, it is good to select a paragraphincluding the keyword selected in Step S201 and obtain, as the portionattribute value of the selected keyword, the portion attribute valuewhich has the highest appearance frequency in the selected paragraph. Ingeneral, in the image interpretation report written for a plurality oforgans, findings regarding each organ are written on a paragraph basis.Since the portion attribute value which has the highest appearancefrequency in the selected paragraph is the corresponding organ, it ispossible to obtain at least the correct name of the organ as the portionattribute value. Here, the paragraph can be detected, for example,regarding a space line or a line feed as a separator between paragraphs.

Next, the keyword attribute obtaining unit 107 associates the keywordselected in Step S201 with the attribute values obtained in Steps S203to S205 (Step S206). For example, when the keyword “stain” is selectedin Step S201 and the sentence including the keyword is “An early stainis recognized in the liver segment S1”, “early phase”, “existence”, and“liver segment S1” are obtained as a phase attribute value, an existenceattribute value, and a portion attribute value, respectively, in StepsS203 to S205. As a result, the keyword and attribute values areintegrated into a combination of (Stain, Early phase, Existence, Liversegment S1).

Lastly, the keyword attribute obtaining unit 107 determines whether ornot all the keywords obtained from the keyword extracting unit 106 issubject to Step S201. If the result is No, a return is made to StepS201, and if the result is Yes, the processing is completed (Step S207).

By performing Steps S201 to S206 in this way, it is possible to obtainthe at least one combination of the keyword and the attribute values inStep S103.

Here, operations by the similar case searching apparatus 100 shown inFIG. 6 are further described.

The image feature extracting unit 108 extracts image feature quantitiesfrom the medical image group 20 obtained by the interpretation targetobtaining unit 105, and outputs the extracted image feature quantitiesand the medical image group 20 to the search vector generating unit 109(Step S104).

Next, the search vector generating unit 109 generates search vectors forthe medical image group 20 using the combination of the keyword and theattribute value obtained from the keyword attribute obtaining unit 107,the image feature quantities obtained from the image feature extractingunit 108, and the relevance database 104, and outputs the search vectorsto the similar case searching unit 110 (Step S105). More specifically,the search vector generating unit 109 obtains, from the relevancedatabase 104, the degrees of relevance of the image feature quantitieswith the combination of the keyword and the attribute value obtainedfrom the keyword attribute obtaining unit 107. The search vectorgenerating unit 109 performs weighting on the image feature quantitiesobtained from the image feature extracting unit 108 by multiplying theimage feature quantities with the obtained degrees of relevance asweights to the image feature quantities. For example, it is assumed thatan average luminance value in the medical images, an average luminancevalue in central areas of the medical images, and an average luminancevalue in peripheral areas of the medical images are obtained as theimage features in Step S104. At this time, the values of the obtainedimage feature quantities are presented as an image feature quantityvector (100, 50, 150). Likewise, degrees of relevance for an averageluminance value in the medical images, an average luminance value incentral areas of the medical images, and an average luminance value inperipheral areas of the medical images obtained from the relevancedatabase 104 can be presented as a vector (0.8, 0.5, 0.2). In this case,the image features after weighting are obtained as (80, 25, 30) throughthe multiplication. In this way, the vector is generated as a searchvector. It is to be noted that a search vector is generated for each ofa first medical image group of first medical images in a case (a firstcase) which is an interpretation target and for each of second medicalimage groups of second medical images in a case (a second case) storedin the case database 101. Image feature quantities, keywords, andattribute values may be registered in advance in the case database 101,for the cases stored in the case database 101. In addition, theinterpretation target obtaining unit 105 may obtain some of the casesregistered in the case database 101, and the image feature extractingunit 108, the keyword extracting unit 106, and the keyword attributeobtaining unit 107 may perform extraction or obtainment processes.

Next, the similar case searching unit 110 searches out a second casehaving a high degree of similarity with the first case as theinterpretation target, from among the cases stored in the case database101, using the search vectors obtained from the search vector generatingunit 109, and outputs the image interpretation report ID 22 of thesearched-out second case to the output unit 111 (Step S106). A specificmethod for calculating degrees of similarity is to calculate, as thedegrees of similarity, a cosine distance between the search vector forthe first medical image group included in the first case obtained fromthe search vector generating unit 109 and the search vectors for thesecond medical image groups included in the second cases stored in thecase database 101.

Lastly, the output unit 111 outputs, to an output-destination medium,case data corresponding to the image interpretation report ID 22obtained from the similar case searching unit 110 (Step S107).

FIG. 8 is a diagram showing an example of a screen of theoutput-destination medium such as a liquid display which displays outputfrom the output unit 111. As shown in FIG. 8, the output unit 111presents similar cases in a descending order of degrees of similarityfor a diagnosis by the image interpreter.

In addition, the output unit 111 may classify the cases searched out bythe similar case searching unit 110 into case data item groups eachbased on similar diseases names, and display the respective case dataitem groups. FIG. 9 shows a classified version of the output example inFIG. 8, in which the search results are classified into the case dataitem groups each classified based on similar disease names and thendisplayed. The image interpreter needs to find out, using the results ofsearching the similar cases, disease name descriptions different fromthe diagnosis by himself or herself from the findings in the searchresults when considering a possibility of a disease other than thedisease as the result of the diagnosis by himself or herself. Bypresenting a display screen for the respective case data item groupseach classified based on similar disease names as the search results,the image interpreter can easily check the disease names in the casespresented as the search results. Therefore, it is possible to reduceimage interpretation time.

Through Steps S101 to S107 shown in FIG. 6 executed as described above,the similar case searching apparatus 100 can search out the similarcases based on the viewpoint of the image interpreter for the result ofthe diagnosis in a userfriendly manner.

In addition, the interpretation target obtaining unit 105 does notalways need to obtain a medical image group 20 and an imageinterpretation report 21 from the case database 101. For example, theinterpretation target obtaining unit 105 may obtain, from anothersystem, a medical image group 20 interpreted just before by the imageinterpreter and the image interpretation report thereof.

In addition, the similar case searching apparatus 100 may search thecase database 101 for only case data items as search targets in each ofwhich image findings 24 and a definitive diagnosis 25 match. The casedatabase 101 includes the medical images based only on which it isimpossible to indicate a lesion that matches the definitive diagnosis,due to image noise or characteristics of an imaging device. There is ahigh possibility that it is difficult to estimate a lesion based only onsuch medical images. Thus, presentation of such similar case data itemsmay increase the risk of a misdiagnosis. In contrast, each of case dataitems in which image findings 24 and a definitive diagnosis 25 match isa case data item which guarantees that it is possible to point out thesame lesion as the lesion in the definitive diagnosis from the medicalimages. Thus, the case data item is appropriate as a similar case dataitem. Thus, by determining, as the search targets, only the case dataitems in each of which image findings 24 and a definitive diagnosis 25match, it is possible to reduce the risk of a misdiagnosis.

In addition, the case database 101, the keyword dictionary 102, theattribute dictionary 103, and the relevance database 104 may be includedin the similar case searching apparatus 100.

In addition, the case database 101, the keyword dictionary 102, theattribute dictionary 103, and the relevance database 104 may be providedon a server connected to the similar case searching apparatus 100 via anetwork.

In addition, the image interpretation report 21 may be attached assupplemental data to the medical image group 20.

As described above, the similar case searching apparatus 100 accordingto this embodiment can search out the similar cases based on theviewpoint of the image interpreter for the result of the diagnosis bythe image interpreter, even for the case in which the single imageinterpretation report is assigned to the plurality of medical images.

In other words, weighting is performed on the image feature quantitiesin the generation of the search vectors. At this time, the weights usedin the weighting are the degrees of relevance between (i) thecombination of the keyword and the attribute value of the keywordextracted from the image interpretation report included in the firstcase and (ii) the image feature quantities. The keyword and attributevalue are values indicating the viewpoint of the image interpreter. Forthis reason, it is possible to search out the similar cases based on theviewpoint of the image interpreter. In addition, the attribute valueindicates the supplemental concept of the keyword. For this reason, theattribute value is the clue for finding out the first medical imagebased on which the image interpretation report was written among themedical images in the first medical image group. With the clue, it ispossible to search out the similar cases based on the viewpoint of theimage interpreter even for the case in which the single text item isassigned to the medical images.

Embodiment 2

Next, a relevance database generating apparatus according to Embodiment2 is described.

The relevance database generating apparatus in this embodiment has afeature of automatically generating a relevance database 104 from thecase database 101.

The similar case searching apparatus 100 according to Embodiment 1performs the similar case searching method using the relevance database104 generated in advance. The relevance database 104 needs to begenerated before use of the similar case searching apparatus 100.

The relevance database generating apparatus in this embodimentcalculates degrees of relevance between (i) combinations of a keywordand attribute values thereof and (ii) image feature quantities, usingmedical images and a case data item obtained from the case database 101,and writes the calculated degrees of relevance to the relevance database104.

In this way, the relevance database generating apparatus canautomatically generate the relevance database 104 before use of thesimilar case searching apparatus 100.

With reference to FIG. 10 at first, structural elements of the relevancedatabase generating apparatus are sequentially described below.

Embodiment 2 Explanation of Structure

FIG. 10 is a block diagram showing a functional structure of a relevancedatabase generating apparatus according to Embodiment 2.

The elements in FIG. 10 which are the same as in those in FIG. 1 areassigned with the same reference signs, and the same descriptions arenot repeated below. The relevance database generating apparatus 200shown in FIG. 10 differs from the similar case searching apparatus 100in the point of including an image feature attribute obtaining unit 201,a same attribute data generating unit 202, a relevance calculating unit203, and a writing unit 204.

The image feature attribute obtaining unit 201 obtains attribute valuescorresponding to image feature quantities extracted by the image featureextracting unit 108, and outputs the obtained attribute values to thesame attribute data generating unit 202. A specific attribute obtainingmethod is described later.

Next, the same attribute data generating unit 202 generates combinationsof a keyword and image feature quantities both having an same attributevalue, using the keyword and attribute value obtained from the keywordattribute obtaining unit 107 and the image features and attribute valueobtained from the image feature attribute obtaining unit 201, andoutputs the generated combinations to the relevance calculating unit203.

Next, the relevance calculating unit 203 calculates degrees of relevancebetween the keyword and image features both having the same attributevalue, using the combinations of the keyword and image features obtainedfrom the same data generating unit. A specific relevance calculatingmethod is described later.

Lastly, the writing unit 204 writes the degrees of relevance obtainedfrom the relevance calculating unit 203 to the relevance database 104.

Next, descriptions are given of operations performed by the relevancedatabase generating apparatus 200 configured as described above.

Embodiment 2 Explanation of Operations

FIG. 11 is a flowchart of overall processes performed by the relevancedatabase generating apparatus 200. The processing in S101 to S104 is thesame as in the processing in S101 to S104 shown in FIG. 6, and thus thesame descriptions are not repeated below.

The image feature attribute obtaining unit 201 obtains the attributevalues corresponding to the image feature quantities obtained from theimage feature extracting unit 108, and outputs the obtained attributevalues to the same attribute data generating unit 202 (Step S301). Morespecifically, the image feature attribute obtaining unit 201 obtains oneor both of the time phase attribute value and the portion attributevalue.

As shown in FIG. 12, a specific method for obtaining the time phaseattribute value is to prepare in advance a data table in which imagecapturing times and time phase attribute values are associated with eachother, and obtain the attribute values of the time phase attributesaccording to the image capturing times. In an example case, the timephase attribute value at the image capturing start time of aninterpretation-target medical image is a “simple phase”, and the timephase attribute value while another interpretation-target medical imageis being captured from 1 to 80 seconds after the image capturing starttime is an “arterial phase”. In addition, in the case of a routinemedical test such as a periodic medical check, it is also good to obtaintime phase attribute values such as a “simple phase”, an “arterialphase”, and an “equilibrium phase”, in accordance with the order ofcapturing the interpretation-target medical images. In addition, it isalso possible to obtain time phase attribute values from image featurequantities such as luminance values of images of blood vessels.

In addition, it is possible to automatically obtain portion attributevalues for the image feature quantities obtained from the image featureextracting unit 108 by performing, on the interpretation-target medicalimages from which image feature quantities are to be extracted, aparticular image processing method for obtaining portion attributevalues. The particular method is, for example, described in Non-patentLiterature: “A method for extracting multi organs by estimating CT valuedistributions from four phase 3D abdominal CT images” by Sakashita,Deguchi, Kitasaka, Mori, and Suenaga, Technical Report of the Instituteof Electronics, Information and Communication Engineers, medical images,Vol. 106, No. 145, pp. 49-54, July, 2006.

Next, the same attribute data generating unit 202 generates combinationsof an image feature quantity and a keyword both having a commonattribute value, using a combination of a keyword and an attribute valueobtained from the keyword attribute obtaining unit 107 and combinationsof an image feature quantity and an attribute value obtained by theimage feature attribute obtaining unit 201. The same attribute datagenerating unit 202 outputs the generated combinations to the relevancecalculating unit 203 (Step S302).

Next, the relevance calculating unit 203 calculates degrees of relevancebetween the image feature quantities and keyword, using the combinationsobtained from the same attribute data generating unit 202, and outputsthe calculated degrees of relevance to the writing unit 204 (Step S303).An exemplary method for calculating the degrees of relevance is tocalculate the correlation ratios between the image feature quantitiesand the keyword. A method for calculating the correlation ratio betweeneach of the image feature quantities and the keyword paired with eachother is described in detail below.

The correlation ratio is an indicator indicating the correlationrelationship between a qualitative data item and a quantitative dataitem, and is presented in Expression 1 below.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack & \; \\{\eta^{2} = {\frac{\sum\limits_{i}^{\;}\;{N_{i}\left( {{\overset{\_}{x}}_{i} - \overset{\_}{x}} \right)}^{2}}{\sum\limits_{i}^{\;}{\sum\limits_{j}^{\;}\;\left( {x_{ij} - \overset{\_}{x}} \right)^{2}}} = \frac{S_{B}}{S_{T}}}} & \left( {{Expression}\mspace{14mu} 1} \right)\end{matrix}$

Here, x_(ij) is an i-th observed value that belongs to a category i ofthe qualitative data;

x _(i) denotes the average value of observed values that belong to thecategory i of the qualitative data;

x denotes the overall average value;

N_(i) denotes the number of observations that belong to the category i;

S_(B) denotes an inter-category dispersion; and

S_(T) denotes a total dispersion.

Image interpretation reports are classified into two categories based onthe presence or absence of a certain keyword, and these categories areassumed to be qualitative data items. The raw values of image featurequantities of a kind extracted from the interpretation-target medicalimages are assumed to be qualitative data items. For example, for eachof the cases included in the case database 101, the image interpretationreports are classified into the categories one of which includes imageinterpretation reports including a combination of the certain keywordand attribute value thereof and the other of which includes imageinterpretation reports without including such combination. Here, adescription is given of an approach for calculating the correlationratios between a keyword “Stain, Arterial phase” and an image featurequantity “Average luminance value inside a tumor, Arterial phase”. It isto be noted here that the keyword and the image feature quantity areassigned with the common attribute value “Arterial phase”. For thisreason, calculating the correlation ratio between the keyword “Stain,Arterial phase” and the image feature quantity “Average luminance valueinside a tumor, Arterial phase” is equivalent to calculating thecorrelation ratio between (i) the combination of the keyword “Stain” andthe attribute value “Arterial phase” and (ii) the image feature quantity“Average luminance value inside a tumor”. In Expression 1, it is assumedthat the category i=1 includes the “Stain, Arterial phase”, and that thecategory i=2 does not include the “Stain, Arterial phase”. Here, x1jdenotes the j-th observed value that is the “Average luminance valueinside a tumor, Arterial phase” in the tumor image extracted from a casewhose image interpretation report includes the “Stain, Arterial phase”.Here, x2j denotes the j-th observed value that is the “Average luminancevalue inside a tumor, Arterial phase” in the tumor image extracted froma case whose image interpretation report does not include the “Stain,Arterial phase”. The “Stain, Arterial phase” indicates that a CT valueincreases in the arterial phase in the contrast radiography, and thusthe correlation ratio is expected to be increased (to a value closeto 1) in this case. Furthermore, the stain depends on the type of thetumor, but does not depend on the size of the tumor, and thus thecorrelation between the keyword “Stain, Arterial phase” and an imagefeature quantity “Tumor size” is small (a value close to 0). In thisway, the correlation ratios between all the pairs of a keyword and animage feature quantity are calculated.

FIG. 13 is a conceptual chart of correlation ratios between (i) thecombination of the keyword and the attribute value and (ii) the imagefeature quantities. In this chart, the correlation ratios are shown in amulti-value representation in which the boldness of the solid linescorresponds to the magnitudes of the correlation rations. For example,the highest correlation is observed between the “Stain, Arterial phase”related to the arterial phase in which the CT value increases in thecontrast radiography and the average luminance value (abbreviated as“Average luminance in arterial phase” in FIG. 13) inside the tumor inthe arterial phase.

Focusing on these values of the correlation ratios makes it possible toidentify the image feature quantities highly related to the combinationof the certain keyword and the attribute value. In reality, it is highlylikely that one case includes a plurality of lesions (tumors) and forwhich a plurality of images are captured. The image interpretationreport of the case includes descriptions about the lesions. For example,in a contrast CT scan, CT images are captured at plural time pointsbefore and after the application of a contrast medium. For this reason,sets of slice images are obtained, each of the sets of slice imagesincludes plural lesions (tumors), and a plurality of image featurequantities are extracted from each of the lesions. For this reason, thenumber of image feature quantities is obtained according to theExpression (the number of sets of slice images)×(the number of lesionsdetected from a patient)×(the number of kinds of image featurequantities). In addition, it is necessary to calculate the correlationsbetween (i) the image feature quantities and (ii) the imageinterpretation items and the disease name extracted from the imageinterpretation report.

Up to this point, the method for calculating the correlation ratiobetween the keyword and each of the image feature quantities has beendescribed.

Hereinafter, operations by the relevance database generating apparatus200 shown in FIG. 11 are further described.

Lastly, the writing unit 204 writes the degrees of relevance obtainedfrom the relevance calculating unit 203 to the relevance database 104(Step S304).

As described above, the relevance database generating apparatus 200according to this embodiment can calculate the degrees of relevancebetween the keyword and image feature quantities obtained from the casedatabase 101, and thus can automatically generate the relevance database104 before use by the similar case searching unit 100.

FIG. 14 shows the structural relationship between the similar casesearching apparatus 100 according to Embodiment 1 and the relevancedatabase generating unit 200 according to Embodiment 2. As shown in FIG.14, the similar case searching apparatus 100 and the relevance databasegenerating unit 200 are connected via the relevance database 104, thecase database 101, the keyword dictionary 102, and the attributedictionary 103.

Embodiment 3

Next, a relevance database generating apparatus according to Embodiment3 is described.

The relevance database generating apparatus in this embodiment has afeature of automatically generating a relevance database 104 from a casedatabase 101.

The relevance database generating apparatus 200 according to Embodiment2 performs the automatic relevance calculating method when the casedatabase 101 is provided. Here, the case database 101 is characterizedin that diagnosis results are accumulated therein day by day tosequentially update the data stored therein. When an imageinterpretation report including a keyword which is not included in therelevance database 104 is newly added to the case database 101, thedegree of relevance for the newly added keyword has not yet beencalculated. For this reason, it is impossible to perform any searchusing the keyword. This causes a problem that the similar case searchingapparatus 100 cannot search out similar cases based on a viewpoint of animage interpreter.

To solve this problem, the relevance database generating apparatusaccording to this embodiment calculates the degrees of relevance betweenthe combination of the new keyword and an attribute value thereof andimage feature quantities, in response to the update in the case database101, and writes the calculation results to the relevance database 104.

In this way, even when the case database 101 is updated, it is possibleto search out the similar cases based on the viewpoint of the imageinterpreter.

With reference to FIG. 15 at first, structural elements of the relevancedatabase generating apparatus are sequentially described below.

Embodiment 3 Explanation of Structure

FIG. 15 is a block diagram showing a functional structure of a relevancedatabase generating apparatus according to Embodiment 3.

The elements in FIG. 15 which are the same as in those in FIG. 10 areassigned with the same reference signs, and the same descriptions arenot repeated below. The relevance database generating apparatus 300shown in FIG. 15 differs from the relevance database generatingapparatus 200 shown in FIG. 10 in the point of including an updatecontrol unit 301 which determines whether or not to update the relevancedatabase 104 based on a case obtained from the case database 101.

The update control unit 301 determines whether or not to update therelevance database 104 using the medical images and the case data itemobtained from the case database 101. The update control unit 301 updatesthe relevance database 104 when the answer is Yes. When the answer isNo, the update control unit 301 does not update the relevance database104. A specific determining method is described later. When determiningthe update of the relevance database 104, the update control unit 301causes an interpretation target obtaining unit 105 to obtain the casedata item from the case database 101.

Next, descriptions are given of operations performed by the relevancedatabase generating apparatus 300 configured as described above.

Embodiment 3 Explanation of Operations

FIG. 16 is a flowchart of overall processes performed by the relevancedatabase generating apparatus 300. The elements in FIG. 16 which are thesame as in those in FIG. 11 are assigned with the same reference signs,and the same descriptions are not repeated below.

The update control unit 301 determines whether or not to update therelevance database 104 using the case obtained from the case database101. The processing advances to Step S101 when the answer is Yes, andotherwise, the processing is completed (Step S401).

As an exemplary specific update determining method, it is good tosequentially update the relevance database 104 each time the casedatabase 101 is updated. In other words, when the case database 101 isupdated, the update control unit 301 causes the interpretation targetobtaining unit 105 to obtain a plurality of medical images and imageinterpretation reports included in all the case data items included inthe case database 101.

In addition, as another exemplary update determining method, it is alsogood to count up the appearance frequency of each of keywords includedin the case database 101, and update the relevance database 104 whenupdate is made for a keyword having an appearance frequency that is nomore than a threshold value among the keywords in the case database 101.In other words, the update control unit 301 causes the interpretationtarget obtaining unit 105 to obtain, at the time of update of the casedatabase 101, one or more image interpretation reports each including akeyword having an appearance frequency no more than the threshold valueamong all the image interpretation reports and medical imagescorresponding to the one or more image interpretation reports stored inthe case database 101. The relevance database 104 stores the degrees ofrelevance of the respective keywords. When a keyword included in thecase database 101 has a sufficiently large appearance frequency, thedegree of relevance of the keyword has already been calculated using asufficient number of data items. If a keyword having a high appearancefrequency is newly added and the appearance frequency is re-calculated,the value does not change so much, and thus the necessity of updatingthe degree of relevance is low. In the opposite case of a keyword havinga low appearance frequency, the degree of relevance is highly likely tobe inaccurate, and thus the necessity of updating the degree ofrelevance is high. In this way, by determining whether or not update isallowed according to the appearance frequency of the keyword in the casedatabase, it is possible to reduce the calculation amount at the time ofupdate, and to thereby reduce the update time.

As described above, the relevance database generating apparatus 300according to this embodiment can update the degrees of relevance of akeyword and image feature quantities even when the case database 101 isupdated, and can search out similar cases based on a viewpoint of theimage interpreter.

Although the case searching apparatus and relevance database generatingapparatus according to embodiments of the present disclosure have beendescribed above, these embodiments are non-limiting exemplaryembodiments. Those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiment and otherembodiments are possible by arbitrarily combining the structuralelements of the embodiment with others without materially departing fromthe novel teachings and advantages in the present disclosure.Accordingly, all such modifications are intended to be included withinthe scope of the present disclosure.

In the aforementioned similar case searching apparatus 100, the keywordattribute obtaining unit 107 extracts attribute values based on keywordsextracted from image interpretation reports. However, when it isimpossible to extract attribute values based on keywords, it is alsogood to obtain attribute values from medical images auxiliarily. Theobtainment of these attribute values is performed in the same manner asthe obtainment of attribute values by the image feature attributeobtaining unit 201 of the relevance database generating apparatus 200.The search vector generating unit 109 generates search vectors using theattribute values obtained from the medical images instead of attributevalues which are otherwise obtained by the keyword attribute obtainingunit 107, and the similar case searching unit 110 searches out similarcases using the generated search vectors.

The aforementioned similar case searching apparatus or any one of therelevance database generating apparatuses may be configured as acomputer system including a microprocessor, a Read Only Memory (ROM), aRandom Access Memory (RAM), a hard disk drive, a display unit, akeyboard, a mouse, and so on. The RAM or hard disk drive stores acomputer program. The similar case searching apparatus or the relevancedatabase generating apparatus attains its functions through themicroprocessor's operations according to the computer program. Here, thecomputer program is configured to combine a plurality of instructioncodes for giving instructions to the computer for allow the computer toattain its functions.

FIG. 17 is a block diagram showing a hardware structure of a computersystem which includes either the similar case searching apparatusaccording to Embodiment 1 or the relevance database generating apparatusaccording to Embodiments 2 or 3.

The similar case searching apparatus or the similar case searchingapparatus includes: a computer 434; a keyboard 436 and a mouse 438 forgiving instructions to the computer 434; a display 432 for presentinginformation such as a result of computation by the computer 434; and aCD-ROM device 440 and a communication modem (not shown) for reading theprogram to be executed by the computer 434.

The program performed by either the similar case searching apparatus orthe relevance database generating apparatus is recorded onto a CD-ROM442 which is a computer-readable recording medium, and is read by aCD-ROM device 440. Alternatively, the program is read by a communicationmodem 452 via a computer network.

The computer 434 includes: a Central Processing Unit (CPU) 444; a ROM446; a RAM 448; a hard disk 450; a communication modem 452; and a bus454.

The CPU 444 executes the program read through the CD-ROM device 440 orthe communication modem 452. The ROM 446 stores the program or datanecessary for operations by the computer 434. The RAM 448 stores datasuch as parameters at the time of the execution of the program. The harddisk 450 stores the program or data, etc. The communication modem 452communicates with other computers via a computer network. The bus 454establishes mutual connections of the CPU 444, the ROM 446, the RAM 448,the hard disk 450, the communication modem 452, the display 432, thekeyboard 436, the mouse 438, and the CD-ROM device 440.

Furthermore, some or all of the structural elements of the similar casesearching apparatus or the relevance database generating apparatus maybe configured with a single system Large Scale Integration (LSI). Thesystem LSI is a super-multi-function LSI manufactured by integratingstructural units on a single chip, and is specifically a computer systemconfigured to include a microprocessor, a ROM, a RAM, and so on. The RAMstores a computer program. The system LSI achieves its function throughthe microprocessor's operations according to the computer program.

Furthermore, some or all of the structural elements constituting thesimilar case searching apparatus or the relevance database generatingapparatus may be configured as an IC card which can be attached to anddetached from the similar case searching apparatus or the relevancedatabase generating apparatus, or as a stand-alone module. The IC cardor the module is a computer system composed of a microprocessor, a ROM,a RAM and so on. The IC card or the module may include theaforementioned super-multi-function LSI. The IC card or the moduleachieves its function through the microprocessor's operations accordingto the computer program. The IC card or the module may also beimplemented to be tamper-resistant.

In addition, an embodiment of the present disclosure may be any one ofthe methods described above. In addition, the present disclosure may berealized as a computer program for causing a computer to execute theabove-described method, or as a digital signal of the computer program.

Furthermore, an embodiment of the present disclosure may be realized asa non-transitory computer-readable recording medium having the computerprogram or the digital signal recorded thereon. Examples of therecording medium include a flexible disc, a hard disk, a CD-ROM, an MO,a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray (registered trademark) Disc),and a semiconductor memory. In addition, the present disclosure may berealized as the digital signal recorded on the non-transitorycomputer-readable recording medium.

Furthermore, an embodiment of the present disclosure may be realized asthe aforementioned computer program or digital signal transmitted via atelecommunication line, a wireless or wired communication line, anetwork represented by the Internet, a data broadcast, and so on.

In addition, an embodiment of the present invention may be a computersystem including a microprocessor and a memory. The memory may store theprogram and the microprocessor may operates according to the computerprogram.

Furthermore, it is also possible to execute another independent computersystem by transmitting the program or the digital signal recorded on theaforementioned non-transitory computer-readable recording media, or bytransmitting the program or digital signal via the aforementionednetwork and the like.

Each of the structural elements in each of the above-describedembodiments may be configured in the form of an exclusive hardwareproduct, or may be realized by executing a software program suitable forthe structural element. Each of the structural elements may be realizedby means of a program executing unit, such as a CPU and a processor,reading and executing the software program recorded on a recordingmedium such as a hard disk or a semiconductor memory.

Here, the software program for realizing a similar case searching methodaccording to each of the embodiments is a program described below.

The program causes a computer to execute the similar case searchingmethod for searching out, from a case database, a second case data itemsimilar to a first case data item including a first medical image groupof first medical images and a first image interpretation report which isa text data item indicating a result of interpreting the first medicalimage group, the similar case searching method being executed by acomputer and including: extracting a plurality of image featurequantities from the first medical image group; extracting a keyword fromthe first image interpretation report, the keyword being either (a) animage interpretation item which is a character string indicating afeature of at least one of the first medical images or (b) a diseasename which is a result of a diagnosis made by a user based on the firstmedical images; obtaining an attribute value which is a word indicatinga supplemental concept of the keyword, from a sentence including thekeyword extracted in the extracting; with reference to a relevancedatabase storing the degrees of relevance between (i) a combination ofthe keyword extracted in the extracting and the attribute value of thekeyword obtained in the obtaining and (ii) the respective image featurequantities extracted in the extracting, performing weighting on (i) theimage feature quantities extracted in the extracting and (ii) imagefeature quantities extracted from a second medical image group of secondmedical images included in the at least one second case data item storedin the case database, using the degrees of relevance as weights; andgenerating a search vector for the first medical image group and asearch vector for the second medical image group, each of the searchvectors having, as elements, corresponding ones of image featurequantities resulting from the weighting; and searching out the at leastone second case data item stored in the case database, based on a degreeof similarity between the search vector for the first medical imagegroup and the search vector for the second medical group.

Here, the software program for realizing a relevance database generatingmethod according to each of the embodiments is a program describedbelow.

The program causes a computer to execute the relevance databasegenerating method, including: extracting image feature quantities from aplurality of medical images; obtaining attribute values of therespective image feature quantities extracted in the extracting, fromthe plurality of medical images; extracting, as a keyword, either animage interpretation item or a disease name, from an imageinterpretation report which is a text data item describing a result ofinterpretation of the medical images by a user, the image interpretationitem being a character string indicating a feature of at least one ofthe medical images, and the disease name being a result of a diagnosismade by the user based on the medical images; obtaining an attributevalue of the keyword, from a sentence including the keyword extracted inthe extracting; generating a combination of the keyword and each ofimage feature quantities both having a same attribute value, based on(i) the keyword and the attribute value of the keyword extracted fromthe image interpretation report and (ii) the image feature quantitiesextracted from the medical images and the attribute values of therespective image feature quantities; and calculating, from thecombination of the keyword and each of the image feature quantities bothhaving the same attribute value, a degree of relevance between thekeyword and the image feature quantity, and generating a relevancedatabase indicating a degree of relevance between (i) the combination ofthe keyword and the attribute value of the keyword and (ii) the imagefeature quantity.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a similar case searchingapparatus or the like which outputs similar cases for diagnosis resultsby image interpreters. In addition, the present disclosure is applicableas a relevance database generating apparatus or the like which generatesrelevance database for use by the similar case searching apparatus.

The invention claimed is:
 1. A similar case searching apparatus whichsearches out, from a case database, at least one second case data itemsimilar to a first case data item including a first medical image groupof first medical images and a first image interpretation report which isa text data item indicating a result of interpreting the first medicalimage group, the similar case searching apparatus comprising: amicroprocessor; and a non-transitory memory having stored thereonexecutable instructions, which when executed by the microprocessor,cause the similar case searching apparatus to function as: an imagefeature extracting unit configured to extract a plurality of imagefeature quantities from the first medical image group; a keywordextracting unit configured to extract a keyword from the first imageinterpretation report, the keyword being either (a) an imageinterpretation item which is a character string indicating a feature ofat least one of the first medical images or (b) a disease name which isa result of a diagnosis made by a user based on the first medicalimages; a keyword attribute obtaining unit configured to (i) search fora plurality of attribute values, the attribute values including a timephase attribute value, an existence attribute value, and a portionattribute value, and (ii) obtain at least one of the attribute valueswhich is a word indicating a supplemental concept of the keyword, from asentence including the keyword extracted by the keyword extracting unit;a search vector generating unit configured to: with reference to arelevance database storing the degrees of relevance between (i) acombination of the keyword extracted by the keyword extracting unit andthe at least one attribute value of the keyword obtained by the keywordattribute obtaining unit and (ii) the respective image featurequantities extracted by the image feature extracting unit, performweighting on (i) the image feature quantities extracted by the imagefeature extracting unit and (ii) image feature quantities extracted froma second medical image group of second medical images included in the atleast one second case data item stored in the case database, using thedegrees of relevance as weights; and generate a search vector for thefirst medical image group and a search vector for the second medicalimage group, each of the search vectors having, as elements,corresponding ones of image feature quantities resulting from theweighting; and a similar case searching unit configured to search outthe at least one second case data item stored in the case database,based on a degree of similarity between the search vector for the firstmedical image group and the search vector for the second medical group.2. The similar case searching apparatus according to claim 1, whereinthe time phase attribute value indicates a relative image capturing timeof a corresponding one of the first medical images in the first medicalimage group or a relative image capturing time period in which thecorresponding first medical image is captured, from the sentenceincluding the keyword extracted by the keyword extracting unit.
 3. Thesimilar case searching apparatus according to claim 1, wherein theexistence attribute value indicates existence or non-existence ofinformation in the at least one of the first medical images, from thesentence including the keyword indicating the information and extractedby the keyword extracting unit.
 4. The similar case searching apparatusaccording to claim 1, wherein the portion attribute value indicates aportion of an organ whose image is to be interpreted, from the sentenceincluding the keyword extracted by the keyword extracting unit.
 5. Thesimilar case searching apparatus according to claim 1, wherein theexecutable instructions, when executed by the microprocessor, cause thesimilar case searching apparatus to further function as an output unitconfigured to output the at least one second case data item searched outby the similar case searching unit to outside the similar case searchingapparatus.
 6. The similar case searching apparatus according to claim 5,wherein the output unit is configured to classify each of the at leastone second case data item searched out by the similar case searchingunit into a corresponding one of case data item groups each based onsimilar diseases names, and output the classified at least one secondcase data item to outside the similar case searching apparatus.
 7. Thesimilar case searching apparatus according to claim 1, wherein thesimilar case searching unit is configured to search for only one or moresecond case data items each assigned with an image interpretation reportin which an image finding and a definitive diagnosis match from amongsecond case data items stored in the case database, the image finding isa result of a diagnosis made by an image interpreter based on the secondmedical image group included in the second case data item, and thedefinitive diagnosis is a final diagnosis result obtained based on thesecond medical image group included in the second case data item.
 8. Arelevance database generating apparatus, comprising: a microprocessor;and a non-transitory memory having stored thereon executableinstructions, which when executed by the microprocessor, cause therelevance database generating apparatus to function as: an image featureextracting unit configured to extract image feature quantities from aplurality of medical images; an image feature attribute obtaining unitconfigured to obtain attribute values of the respective image featurequantities extracted by the image feature extracting unit, from theplurality of medical images; a keyword extracting unit configured toextract, as a keyword, either an image interpretation item or a diseasename, from an image interpretation report which is a text data itemdescribing a result of interpretation of the medical images by a user,the image interpretation item being a character string indicating afeature of at least one of the medical images, and the disease namebeing a result of a diagnosis made by the user based on the medicalimages; a keyword attribute obtaining unit configured to (i) search fora plurality of attribute values of the keyword, the attribute valuesincluding a time phase attribute value, an existence attribute value,and a portion attribute value, and (ii) obtain at least one of theattribute values of the keyword, from a sentence including the keywordextracted by the keyword extracting unit; a same attribute datagenerating unit configured to generate a combination of the keyword andeach of image feature quantities both having a same attribute value,based on (i) the keyword and the at least one attribute value of thekeyword extracted from the image interpretation report and (ii) theimage feature quantities extracted from the medical images and theattribute values of the respective image feature quantities; and arelevance calculating unit configured to calculate, from the combinationof the keyword and each of the image feature quantities both having thesame attribute value, a degree of relevance between the keyword and theimage feature quantity, and generate a relevance database indicating adegree of relevance between (i) the combination of the keyword and theat least one attribute value of the keyword and (ii) the image featurequantity.
 9. The relevance database generating apparatus according toclaim 8, wherein the time phase attribute value indicates a relativeimage capturing time of a corresponding one of the medical images or arelative image capturing time period in which the corresponding medicalimage is captured, from the sentence including the keyword extracted bythe keyword extracting unit.
 10. The relevance database generatingapparatus according to claim 8, wherein the existence attribute valueindicates existence or non-existence of information shown by thekeyword, from the sentence including the keyword, the keyword indicatingthe feature of the at least one of the medical images and extracted bythe keyword extracting unit.
 11. The relevance database generatingapparatus according to claim 8, wherein the portion attribute valueindicates a portion of an organ whose image is to be interpreted, fromthe sentence including the keyword extracted by the keyword extractingunit.
 12. The relevance database generating apparatus according to claim8, wherein the image feature attribute obtaining unit is configured toobtain time phase attribute values as the attribute values of therespective image feature quantities extracted by the image featureextracting unit, from image capturing times of the respective medicalimages, with reference to a data table in which the image capturingtimes of the medical images and the time phase attribute values areassociated with each other.
 13. The relevance database generatingapparatus according to claim 8, wherein the executable instructions,when executed by the microprocessor, cause the relevance databasegenerating searching apparatus to further function as: an interpretationtarget obtaining unit configured to obtain a plurality of medical imagesand an image interpolation report corresponding to the medical imagesincluded in a case data item, from a case database storing the case dataitem; and an update control unit configured to cause the interpretationtarget obtaining unit to obtain, at the time of update of the casedatabase, the medical images and the image interpretation report storedin the case database, wherein the image feature extracting unit isconfigured to extract image feature quantities from the medical imagesobtained by the interpretation target obtaining unit, and the keywordextracting unit is configured to extract the keyword from the imageinterpretation report obtained by the interpretation target obtainingunit.
 14. The relevance database generating apparatus according to claim13, wherein the update control unit is configured to cause theinterpretation target obtaining unit to obtain, at the time of theupdate of the case database, medical images and image interpretationreports included in all case data items included in the case database.15. The relevance database generating apparatus according to claim 13,wherein the update control unit is configured to cause theinterpretation target obtaining unit to obtain, at the time of theupdate of the case database, (i) one or more image interpretationreports each including a keyword having an appearance frequency no morethan a threshold value among all image interpretation reports stored inthe case database and (ii) medical images corresponding to the one ormore image interpretation reports.
 16. A similar case searching methodfor searching out, from a case database, a second case data item similarto a first case data item including a first medical image group of firstmedical images and a first image interpretation report which is a textdata item indicating a result of interpreting the first medical imagegroup, the similar case searching method being executed by a computerand comprising: extracting a plurality of image feature quantities fromthe first medical image group; extracting a keyword from the first imageinterpretation report, the keyword being either (a) an imageinterpretation item which is a character string indicating a feature ofat least one of the first medical images or (b) a disease name which isa result of a diagnosis made by a user based on the first medicalimages; searching for a plurality of attribute values, the attributevalues including a time phase attribute value, an existence attributevalue, and a portion attribute value; obtaining at least one of theattribute values which is a word indicating a supplemental concept ofthe keyword, from a sentence including the keyword extracted in theextracting; with reference to a relevance database storing the degreesof relevance between (i) a combination of the keyword extracted in theextracting and the at least one attribute value of the keyword obtainedin the obtaining and (ii) the respective image feature quantitiesextracted in the extracting, performing weighting on (i) the imagefeature quantities extracted in the extracting and (ii) image featurequantities extracted from a second medical image group of second medicalimages included in the at least one second case data item stored in thecase database, using the degrees of relevance as weights; and generatinga search vector for the first medical image group and a search vectorfor the second medical image group, each of the search vectors having,as elements, corresponding ones of image feature quantities resultingfrom the weighting; and searching out the at least one second case dataitem stored in the case database, based on a degree of similaritybetween the search vector for the first medical image group and thesearch vector for the second medical group.
 17. A relevance databasegenerating method, comprising: extracting image feature quantities froma plurality of medical images; obtaining attribute values of therespective image feature quantities extracted in the extracting, fromthe plurality of medical images; extracting, as a keyword, either animage interpretation item or a disease name, from an imageinterpretation report which is a text data item describing a result ofinterpretation of the medical images by a user, the image interpretationitem being a character string indicating a feature of at least one ofthe medical images, and the disease name being a result of a diagnosismade by the user based on the medical images; searching for a pluralityof attribute values of the keyword, the attribute values including atime phase attribute value, an existence attribute value, and a portionattribute value; obtaining at least one of the attribute values of thekeyword, from a sentence including the keyword extracted in theextracting; generating a combination of the keyword and each of imagefeature quantities both having a same attribute value, based on (i) thekeyword and the at least one attribute value of the keyword extractedfrom the image interpretation report and (ii) the image featurequantities extracted from the medical images and the attribute values ofthe respective image feature quantities; and calculating, from thecombination of the keyword and each of the image feature quantities bothhaving the same attribute value, a degree of relevance between thekeyword and the image feature quantity, and generating a relevancedatabase indicating a degree of relevance between (i) the combination ofthe keyword and the at least one attribute value of the keyword and (ii)the image feature quantity.
 18. A non-transitory computer-readablerecording medium storing a program for causing a computer to execute thesimilar case searching method according to claim
 16. 19. Anon-transitory computer-readable recording medium storing a program forcausing a computer to execute the relevance database generating methodaccording to claim 17.